Land use/land cover prediction and analysis of the middle reaches of the Yangtze River under different scenarios

Ecological response of green spaces to land use change in the Mu Us Desert-Loess Plateau transition zone, China, since the twenty-first century

The Mu Us Desert-Loess Plateau transition zone in China, a fragile ecosystem prone to desertification, has undergone substantial ecological restoration since the early twenty-first century. This study utilized land use/cover data from 2000 to 2023 to assess the ecological response of green spaces to these efforts. A comprehensive set of ecological indices-including the green ecosystem index (GEI), equivalent ecological quality (EEQ) index, and green ecological contribution (GEC) rate-was used to quantify changes in green space extent and ecosystem quality. The study results showed a 5.83% increase in green space area, corresponding to an addition of 1979.7 km2, along with a notable rise in the GEI across 20.18% of the region, reflecting improved ecosystem function and resilience. The conversion of barren into productive green spaces has mitigated land degradation and supported ecological recovery. The EEQ of regional green spaces improved by 1.16%, and the GEC from land use changes was 1.15%. However, challenges remain, including the degradation of 3114.5 km2 of high-quality green spaces and the encroachment of 428.2 km2 by non-green land uses. Ongoing monitoring, targeted interventions, and adaptive management strategies are essential for further improving land greening and ecological quality. This study provides valuable insights for sustainable land management and ecological restoration in similar fragile environments.

Identification of forest priority conservation and restoration areas for different SSPs-RCPs scenarios

The conservation and restoration of forests are a crucial component of climate mitigation strategies in many countries. However, the scientific selection of priority areas for forest conservation and restoration remains a challenge. Based on the landscape indices, the forest landscape structural connectivity index was constructed based on principal component analysis; the forest landscape functional connectivity index was constructed based on the minimum cumulative resistance model. Geodetector was employed to identify the driving forces of forest landscape structural and functional connectivity in the Fujian Delta region. The patch-generating land use simulation model was then used to simulate land use changes under different shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) scenarios from 2030 to 2050. The optimal scenario of forest development was then selected based on the forest landscape structural and functional connectivity. Finally, graph theory was used to identify priority forest conservation and restoration areas under optimal scenarios. The results indicate the following: (1) elevation (q = 0.34, P < 0.01) and nighttime light (q = 0.33, P < 0.01) are the primary drivers of structural connectivity in forested landscapes, while nighttime light (q = 0.38, P < 0.01) and gross domestic product (q = 0.28, P < 0.01) are the primary drivers of functional connectivity in forested landscapes. The joint effect of elevation and nighttime lighting (q = 0.44, P < 0.01) enhances the explanatory power of structural connectivity in forested landscapes. The joint effect of nighttime lighting and gross domestic product (q = 0.46, P < 0.01) enhances the explanatory power of functional connectivity in forested landscapes. (2) Overall, between 2020 and 2050, forest landscape structural and functional connectivity tends to increase in the SSP1-2.6 scenario and decrease in the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. Based on the structural and functional connectivity of the forest landscape, the optimal scenario for future development was identified as SSP1-RCP2.6. (3) The areas of forests prioritized for conservation in 2030, 2040, and 2050 are 12,470.18 km2, 12,470.18 km2, and 12,227.67 km2, respectively. The areas of forests prioritized for restoration are 51.80 km2, 103.14 km2, and 390.86 km2, respectively. This study identified priority forest conservation and restoration areas under SSPs-RCPs scenarios using graph theory, offering valuable insights into biodiversity conservation and the identification of locations for forest conservation and restoration planning.

Changing characteristics, driving factors and future predictions of land use in the Weigan-Kuqa River Delta Oasis, China

The oasis serves as the central component of the arid ecosystem and plays a crucial role in supporting human activities. However, the ecological environment in the oasis region is fragile, and even a minor alteration in land use (LU) can significantly impact the stability of the ecosystem. Therefore, it is imperative to undertake comprehensive research on the spatio-temporal patterns of LU change in the oasis, reveal its driving factors, and predict future development. This is crucial for devising scientifically and logically sound land management strategies, upholding the equilibrium between humans and land in arid areas, and attaining sustainable development of the regional ecology and economy. This study focuses on the Weigan-Kuqa River Delta Oasis in China as the research area, analyzes the changes in LU in the oasis from 2010 to 2022 using various methods such as transition matrix, dynamic degree, intensity analysis, and center of gravity shift. The study also investigates the factors influencing these changes using the optimal parameters-based geographical detector (OPGD). Additionally, it predicts the future trends in LU development under four different scenarios using the mixed-cell cellular automata (MCCA), and illustrates distribution characteristics by combining Moran's I index and hotspot analysis. The results suggest that: (1) Between 2010 and 2022, the LU in the oasis changed rapidly, with consistent increase in the amount of construction land, arable land, and garden land, while the amount of forest-grassland and unused land decreased overall. (2) Population density played a leading role in the changes, but soil type also had a significant impact. Over the course of time, the influence of roads and transportation has progressively increased. (3) Compared with 2022, the acreage of arable land, garden land, and construction land increases under the four future scenarios: natural development scenario (NDS), economic development scenario (EDS), cropland development scenario (CDS), and ecological protection scenario (EPS). However, the acreage of forest-grassland and unused land decrease. From a spatial perspective, large towns, the downstream of alluvial fans, and the central oasis are key areas where the distribution of hot spots and sub-hot spots of each LU type varies significantly among the four scenarios. The EPS provides a certain level of protection for forest-grassland areas and water bodies, making it the most appropriate development model for oases. These findings have the potential to offer valuable academic guidance for oasis land resource management and are crucial for achieving coordinated development at regional level.

Estimation and prediction of water conservation in the upper reaches of the Hanjiang River Basin based on InVEST-PLUS model

With the gradual prominence of global water shortage and other problems, evaluating and predicting the impact of land use change on regional water conservation function is of great reference significance for carrying out national spatial planning and environmental protection, and realizing land intelligent management. We first analyzed 8,416 remote sensing images in the upper reaches of the Hanjiang River Basin (URHRB) by GEE platform and obtained the land use and land cover (LULC) results of fours periods. Through our field investigation, the accuracy of remote sensing image interpretation is obviously higher than that of other comprehensive LULC classification results. Then, through the coupling of InVEST-PLUS model, not only the results of URHRB water conservation from 1990 to 2020 were calculated and the accuracy was assessed, but also the LULC results and water conservation of URHRB under different development scenarios in 2030 were predicted. The results showed as follows: From 1990 to 2020, the forest area of URHRB increased by 7152.23 km2, while the area of cropland, shrub and grassland decreased by 3220.35 km2, 1414.72 km2 and 3385.39 km2, respectively. The InVEST model reliably quantifies the water yield and water conservation of URHRB. In the past 30 years, the total amount of water-saving in China has shown a trend of increasing first and then decreasing. From the perspective of vegetation types, URHRB forest land is the main body of water conservation, with an average annual water conservation depth of 653.87 mm and an average annual water conservation of 472.10×108 m3. Under the ecological protection scenario of the URHRB in 2030, the maximum water conservation in the basin is 574.92×108 m3, but compared with the water conservation in 2010, there is still a gap of 116.28×108 m3. Therefore, through the visualization analysis of the LULC changes of URHRB and water conservation function, it is found that the land and resources department should pay attention to the LULC changes of water sources and adjust the territorial spatial planning in time to cope with the huge water conservation gap in the future.

Evaluation of farmland production potential in key agricultural production areas on the Qinghai-Tibet Plateau under multi-scenario simulation

Predicting changes in future land use and farmland production potential (FPP) within the context of shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) is crucial for devising sustainable land use strategies that balance agricultural production and ecological conservation. Therefore, the Huangshui Basin (HSB) in the northeast Qinghai-Tibet Plateau is taken as the study area, and a LUCC-Plus-FPP (LPF) coupling framework based on the SSP-RCP scenarios is proposed to evaluate future land use patterns and FPP changes. On the basis of the predictions of land use changes from 2020 to 2070, the trade-offs in grain production resulting from bivariate changes in farmland and FPP under future scenarios are analyzed. The results indicate that the model has a high simulation accuracy for land use types, with an overall accuracy of 0.98, a kappa coefficient of 0.97, and a figure of merit value of 0.21. Under the SSP245 and SSP585 scenarios, built-up land increases significantly, by approximately 45.89 %. Farmland and grassland conversions contribute the most to increased built-up land. Farmland area consistently decreases by approximately 5 % across all scenarios. The protection of farmland in the study area is difficult to undertake and thus requires much attention. Moreover, under the SSP126 scenario, the FPP of most districts is greater than that in 2020, and the average FPP of the HSB from 2030 to 2070 is greater than that in 2020. In the SSP585 scenario, by 2070, the average FPP of all districts decreases to different degrees compared with that in 2020. Furthermore, the compensated farmland quantities and average FPPs under all the scenarios are significantly lower than the amount of occupied farmland. The results provide a theoretical foundation and data support for farmland protection decision-making and layout optimization in the Qinghai-Tibet Plateau.

Exploring the dynamics and future projections of land use land cover changes by exploiting geospatial techniques; A case study of the Kabul River Basin

Global land cover change has caused significant environmental degradation and biodiversity loss. It affects ecosystem functions, livelihoods, and climate variation and has drawn substantial attention in recent decades. In the Kabul River Basin (KRB), there are limited studies on the historical Land Use/Land Cover (LULC) pattern, transition, intensity and future perspective. Therefore, this study aims to investigate long-term LULC changes and major drivers of LULC in the KRB over the past thirty years (1990-2020) and then to project the future LULC pattern for the years 2030, 2040 and 2050. Landsat Imageries of (1990-2020) were used as input data by utilizing the Random Forest Classifier algorithm (RF) in the Google Earth Engine (GEE) to classify the LULC. The LULC was then projected for the future, using the Cellular Automata Markov Chain Model (CA-MCM). The results demonstrated drastic LULC changes, controlled primarily by urbanization and agriculture expansion, which expanded from 467 Km2 (0.7 %) to 2312 km2 (3.4 %) and 6528 km2 (9.6 %) to 10812 (15.9 %), between 1990 and 2020. In contrast, bare land decreased from 70606 km2 (82.1 %) to 48212 km2 (70.9 %) between 1990 and 2020. In addition, the study depicts that the expansion in built-up and vegetation areas in the KRB during the study period were at the utilization of bare land. Future LULC predictions indicated that between 2020 and 2050, bare land would trend downward from 48212 km2 (70.9 %) to 46172 km2 (67.9 %), while vegetation and built-up areas would trend upward from 2312 km2 (3.4 %) to 3640 km2 (5.3 %), 10812 km2 (15.9 %) to 11622 km2 (17.1 %), and water bodies and snowcover would slightly vary from 1.2 % to 0.9 % and 7.9 %-9.0 %. In addition, the results of LULC dynamics reveal a significant strong positive correlation between population and built, as well as population and vegetation. Conversely, there is a strong negative correlation between population and bare land. Our results provide precise insights on LULC patterns and trends in the KRB, which could be employed to design a sustainable framework for land use and ecosystem protection.

Climate and land use/land cover changes increasing habitat overlap among endangered crested ibis and sympatric egret/heron species

Climate and land use/land cover (LULC) changes have far-reaching effects on various biological processes in wildlife, particularly interspecific interactions. Unfortunately, interspecific interactions are often overlooked when assessing the impacts of environmental changes on endangered species. In this study, we examined niche similarities and habitat overlaps between wild Crested Ibis and sympatric Egret and Heron species (EHs) in Shaanxi, China, using Ecological niche models (ENMs). We aimed to forecast potential alterations in habitat overlaps due to climate and LULC changes. The results showed that although EHs possess a broader niche breadth compared to the Crested Ibis, they still share certain niche similarities, as indicated by Schoener's D and Hellinger's I values exceeding 0.5, respectively. Notably, despite varying degrees of habitat reduction, the shared habitat area of all six species expands with the changes in climate and LULC. We suggest that with the climate and LULC changes, the habitats of sympatric EHs are likely to suffer varying degrees of destruction, forcing them to seek refuge and migrate to the remaining wild Ibis habitat. This is primarily due to the effective conservation efforts in the Crested Ibis habitat in Yangxian County and neighboring areas. Consequently, due to the niche similarity, they will share and compete for limited habitat resources, including food and space. Therefore, we recommend that conservation efforts extend beyond protecting the Crested Ibis habitat. It is crucial to control human activities that contribute to LULC changes to safeguard the habitats of both Crested Ibis and other sympatric birds.

Prioritizing conservation efforts based on future habitat availability and accessibility under climate change

The potential for species to shift their ranges to avoid extinction is contingent on the future availability and accessibility of habitats with analogous climates. To develop conservation strategies, many previous researchers used a single method that considered individual factors; a few combined 2 factors. Primarily, these studies focused on identifying climate refugia or climatically connected and spatially fixed areas, ignoring the range shifting process of animals. We quantified future habitat availability (based on species occurrence, climate data, land cover, and elevation) and accessibility (based on climate velocity) under climate change (4 scenarios) of migratory birds across the Yangtze River basin (YRB). Then, we assessed species' range-shift potential and identified conservation priority areas for migratory birds in the 2050s with a network analysis. Our results suggested that medium (i.e., 5-10 km/year) and high (i.e., ≥ 10 km/year) climate velocity would threaten 18.65% and 8.37% of stable habitat, respectively. Even with low (i.e., 0-5 km/year) climate velocity, 50.15% of climate-velocity-identified destinations were less available than their source habitats. Based on our integration of habitat availability and accessibility, we identified a few areas of critical importance for conservation, mainly in Sichuan and the middle to lower reaches of the YRB. Overall, we identified the differences between habitat availability and accessibility in capturing biological responses to climate change. More importantly, we accounted for the dynamic process of species' range shifts, which must be considered to identify conservation priority areas. Our method informs forecasting of climate-driven distribution shifts and conservation priorities.

Evolution characteristics and multi-scenario prediction of habitat quality in Yulin City based on PLUS and InVEST models

As a major energy city in China, Yulin City has faced huge challenges to the ecological environment with its rapid economic development and rapid urbanization. Therefore, it is of great significance to study the impact of land use changes on habitat quality. Based on three periods of land use data in Yulin City in 1995, 2005 and 2015, the PLUS model was used to simulate the land use changes in 2015. The measured kappa coefficient was 0.8859, which met the simulation accuracy requirements. By setting development zone boundaries and adjusting parameters, three progressive scenarios are designed to predict the spatial distribution of land use in Yulin City in 2035. The InVEST model was used to analyze the spatiotemporal evolution of Yulin City's habitat quality in the past 20 years and evaluate the distribution of Yulin City's habitat quality under three scenarios after 20 years. The results are as follows: (1) During the study period, construction land in Yulin City expanded rapidly, with an area increase of 380.87 km2 in 20 years, and ecological land gradually shrank. (2) The land use simulation results of Yulin City under various scenarios in 2035 show that future land use changes in Yulin City will mainly be concentrated in the central and western regions. (3) During the study period, the habitat quality of Yulin City was at a medium level and the overall habitat quality showed a downward trend. Spatially, the degree of habitat quality degradation in Yulin City showed a characteristic of gradually decreasing from West to East. (4) By 2035, under the scenario of suitable urban economic development, Yulin City's habitat quality has been improved to a certain extent, which not only protects ecological security but also meets the demand for construction land for urban development. The results of this study help the government better understand the evolution of land use and habitat quality in Yulin City in the past 20 years, and provide theoretical support and reference for the formulation of Yulin City's ecological environment protection policies and the implementation of ecological protection work under the current land spatial planning.

SLMFNet: Enhancing land cover classification of remote sensing images through selective attentions and multi-level feature fusion

Land cover classification (LCC) is of paramount importance for assessing environmental changes in remote sensing images (RSIs) as it involves assigning categorical labels to ground objects. The growing availability of multi-source RSIs presents an opportunity for intelligent LCC through semantic segmentation, offering a comprehensive understanding of ground objects. Nonetheless, the heterogeneous appearances of terrains and objects contribute to significant intra-class variance and inter-class similarity at various scales, adding complexity to this task. In response, we introduce SLMFNet, an innovative encoder-decoder segmentation network that adeptly addresses this challenge. To mitigate the sparse and imbalanced distribution of RSIs, we incorporate selective attention modules (SAMs) aimed at enhancing the distinguishability of learned representations by integrating contextual affinities within spatial and channel domains through a compact number of matrix operations. Precisely, the selective position attention module (SPAM) employs spatial pyramid pooling (SPP) to resample feature anchors and compute contextual affinities. In tandem, the selective channel attention module (SCAM) concentrates on capturing channel-wise affinity. Initially, feature maps are aggregated into fewer channels, followed by the generation of pairwise channel attention maps between the aggregated channels and all channels. To harness fine-grained details across multiple scales, we introduce a multi-level feature fusion decoder with data-dependent upsampling (MLFD) to meticulously recover and merge feature maps at diverse scales using a trainable projection matrix. Empirical results on the ISPRS Potsdam and DeepGlobe datasets underscore the superior performance of SLMFNet compared to various state-of-the-art methods. Ablation studies affirm the efficacy and precision of SAMs in the proposed model.

Interpretable and explainable hybrid model for daily streamflow prediction based on multi-factor drivers

Streamflow time series data typically exhibit nonlinear and nonstationary characteristics that complicate precise estimation. Recently, multifactorial machine learning (ML) models have been developed to enhance the performance of streamflow predictions. However, the lack of interpretability within these ML models raises concerns about their inner workings and reliability. This paper introduces an innovative hybrid architecture, the TCN-LSTM-Multihead-Attention model, which combines two layers of temporal convolutional networks (TCN) followed by one layer of long short-term memory (LSTM) units, integrated with a Multihead-Attention mechanism for predicting streamflow with streamflow causation-driven prediction samples (RCDP), employing local and global interpretability studies through Shapley values and partial dependency analysis. The find_peaks method was used to identify peak flow events in the test dataset, validating the model's generality and uncovering the physical causative patterns of streamflow. The results show that (1) compared to the LSTM model with the same hyperparameter settings, the proposed TCN-LSTM-Multihead-Attention hybrid model increased the R2 by 52.9%, 2.5%, 43.1%, and 10.7% respectively at four stations in the test set predictions using RCDP samples. Moreover, comparing the prediction results of the hybrid model under different samples in Hengshan station, the R2 for RCDP increased by 5.06% and 1.22% compared to streamflow autoregressive prediction samples (RAP) and meteorological-soil volumetric water content coupled autoregressive prediction samples (MCSAP) respectively. (2) Historical streamflow data from the preceding 3 days predominantly influences predictions due to strong autocorrelation, with flow quantity (Q) typically emerging as the most significant feature alongside precipitation (P), surface soil moisture (SSM), and adjacent station flow data. (3) During periods of low and normal flow, historical data remains the most crucial factor; however, during flood periods, the roles of upstream inflow and precipitation become significantly more pronounced. This model facilitates the identification and quantification of various hydrodynamic impacts on flow predictions, including upstream flood propagation, precipitation, and soil moisture conditions. It also elucidates the model's nonlinear relationships and threshold responses, thereby enhancing the interpretability and reliability of streamflow predictions.

Spatio-temporal classification of land use and land cover and its changes in Kerala using remote sensing and machine learning approach

Land use and land cover (LULC) analysis gives important information on how the region has evolved over time. Kerala, a land with an extensive and dynamic history of land-use changes, has, until now, lacked comprehensive investigations into this history. So the current study focuses on Kerala, one of the ecologically diverse states in India with complex topography, through Landsat images taken from 1990 to 2020 using two different machine learning classifications, random forest (RF) and classification and regression trees (CART) on Google Earth Engine (GEE) platform. RF and CART are versatile machine learning algorithms frequently employed for classification and regression, offering effective tools for predictive modelling across diverse domains due to their flexibility and data-handling capabilities. Normalised Difference Vegetation Index (NDVI), Normalised Differences Built-up Index (NDBI), Modified Normalised Difference Water Index (MNDWI), and Bare soil index (BSI) are integral indices utilised to enhance the precision of land use and land cover classification in satellite imagery, playing a crucial role by providing valuable insights into specific landscape attributes that may be challenging to identify using individual spectral bands alone. The results showed that the performance of RF is better than that of CART in all the years. Thus, RF algorithm outputs are used to infer the change in the LULC for three decades. The changes in the NDVI values point out the loss of vegetation for the urban area expansion during the study period. The increasing value of NDBI and BSI in the state indicates growth in high-density built-up areas and barren land. The slight reduction in the value of MNDWI indicates the shrinking water bodies in the state. The results of LULC showed the urban expansion (158.2%) and loss of agricultural area (15.52%) in the region during the study period. It was noted the area of the barren class, as well as the water class, decreased steadily from 1990 to 2020. The results of the current study will provide insight into the land-use planners, government, and non-governmental organizations (NGOs) for the necessary sustainable land-use practices.

Simulating land use change for sustainable land management in China's coal resource-based cities under different scenarios

Land use competition among economic development, food security and ecological protection posed challenges for the sustainable development in resource-based cities, especially those represented by coal resource-based cities in China. Predicting future land use change under the coupled framework of shared socioeconomic pathways and representative concentration pathways (SSP-RCPs) was a crucial step in devising sustainable development strategies. In this study, the patch-generated land use simulation (PLUS) model coupled with SSP-RCP scenarios (SSP126, SSP245, SSP585) was used to predict land use changes from year 2020 to 2060, identify key management regions for balancing the goals of ecological protection and food security, and propose corresponding measures. The results showed that, (1) the selected driving factors and model parameters effectively simulated land use changes with an Overall accuracy of 0.95, a Kappa coefficient of 0.92, a Figure of Merit of 0.16, an Exchange error ≤5.69 %, a Shift error ≤1.04 %, and a Quantity error ≤0.67 %. (2) All the scenarios, it was observed that the grassland continued to decrease by 0.86 % to 7.34 %, and the forest and built-up land continued to increase, of which forest increased by 2.34 % to 4.03 %, and built-up land increased by 21.02 % to 61.08 %. Cropland only increased in SSP585 scenario, by 4.76 %, but declining by 2.93 % in SSP126 and SSP245 scenario. (3) In future scenarios, the expansion of built-up land has escalated the risk of cropland and grassland loss. Based on the distribution of key land use conversions, four categories of prioritized land management regions and corresponding measures have been proposed. This provided a potential pathway to mitigate risks associated with the protection of cropland and ecological land. Therefore, this study was instrumental in understanding the mechanisms of land use changes in coal resource-based cities, and provided a reference for land use planning.

Thriving arid oasis urban agglomerations: Optimizing ecosystem services pattern under future climate change scenarios using dynamic Bayesian network

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide.

Land use and habitat quality change in the Yellow River Basin: A perspective with different CMIP6-based scenarios and multiple scales

Studying the spatial distribution of land use/land cover (LULC) and habitat quality (HQ), influenced by both climate change and socio-economic factors, holds immense importance for fostering ecological sustainability. The previous scale setting was based on changes in granularity and division of spatial ranges, without considering the differences in land quantity structure and spatial expansion under different spatial ranges. Therefore, this study is based on climate and economic data at different spatial scales to determine the various land demands of provinces (YRB-P) and integration of provinces (YRB-I) in the Yellow River Basin, and to limit the expansion of LULC in corresponding regions. At the same time, we have also established three future scenarios representing different development speeds based on the latest path of shared socio-economic development in CMIP6. We found exhibit significant characteristics in ecological responses under combinations of different scales and scenarios. Shandong and Henan Provinces are the main gathering (38.7-41.7%, 24.1-26.5%) and expansion (68.54-85.99 × 102km2, 18.89-34.12 × 102km2) provinces of built-up land under the YRB-P scale, and their HQ (0.260-0.397) are significantly lower than the average HQ (0.619-0.654). Forest land, grassland, and high value regions of HQ show "45°" distribution at two scales, with high and low values clearly clustered (Moran's I is 0.5440-0.580). The HQ evolution region is larger and more dispersed at the YRB-P scale, but accumulates in local areas at the YRB-I scale. In addition, the highest and lowest HQ mean values appear under the low speed development scenario at the YRB-P scale (0.721) and the rapid development scenario at the YRB-I scale (0.689), respectively. This study helps decision-makers control different scales and development scenarios to improve the ecological level of the study area.

Mapping and monitoring land use land cover dynamics employing Google Earth Engine and machine learning algorithms on Chattogram, Bangladesh

Land use land cover change (LULC) significantly impacts urban sustainability, urban planning, climate change, natural resource management, and biodiversity. The Chattogram Metropolitan Area (CMA) has been going through rapid urbanization, which has impacted the LULC transformation and accelerated the growth of urban sprawl and unplanned development. To map those urban sprawls and natural resources depletion, this study aims to monitor the LULC change using Landsat satellite imagery from 2003 to 2023 in the cloud-based remote sensing platform Google Earth Engine (GEE). LULC has been classified into five distinct classes: waterbody, build-up, bare land, dense vegetation, and cropland, employing four machine learning algorithms (random forest, gradient tree boost, classification & regression tree, and support vector machine) in the GEE platform. The overall accuracy (kappa statistics) and the receiver operating characteristic (ROC) curve have demonstrated satisfactory results. The results indicate that the CART model outperforms other LULC models when considering efficiency and accuracy in the designated study region. The analysis of LULC conversions revealed notable trends, patterns, and magnitudes across all periods: 2003-2013, 2013-2023, and 2003-2023. The expansion of unregulated built-up areas and the decline of croplands emerged as primary concerns. However, there was a positive indication of a significant increase in dense vegetation within the study area over the 20 years.

Spatiotemporal foresting of soil erosion for SSP-RCP scenarios considering local vegetation restoration project: A case study in the three gorges reservoir (TGR) area, China

Soil erosion is a common form of land degradation. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides a scenario framework for global socio-economic development and climate change by combining Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). The soil erosion estimation under global climate change and land-use change scenarios provided by CMIP6 is valuable for representing future changes and hotspots. This study estimated the future changes in soil erosion in the Three Gorges Reservoir (TGR) area, China, which has suffered severe soil loss over an extended period, and vegetation restoration projects have been conducted since 1999. The scenarios provided by SSP1-2.6, SSP2-4.5, and SSP5-8.5 were coupled with the scenarios of regional vegetation restoration projects to reflect future land use changes (LUC) and climate change. The results showed that future soil erosion from 2020 to 2100 in the TGR area will experience a non-significant decreasing trend (with trend slopes of -0.013, -0.020, and-0.006 in SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, with p > 0.05). However, with the R factors calculated by different methods, this decreasing trend becomes either insignificant or a significant increasing trend. SSP1-2.6 will experience the lowest soil erosion in 2100 owing to the large amount of forest increase in this scenario. Furthermore, as estimates, the grain-for-green policy (GGP) will reduce 89353.47, 92737.73 and 42916.52 ton soil erosion per year in SSP1-2.6, SSP2-4.5 and SSP3-8.5 by 2100, respectively. In the future, the GGP will become increasingly important for controlling soil loss in the TGR area owing to the increasing precipitation in all scenarios, which increases the risk of soil loss.

Attribution and driving force of nitrogen losses from the Taihu Lake Basin by the InVEST and GeoDetector models

Quantifying temporal and spatial changes in reactive nitrogen (Nr) losses from a watershed and exploring its main drivers are the key to watershed water quality improvements. Huge Nr losses continue to threaten the safety of the water environment in the Taihu Lake Basin (TLB). Here, the InVEST and GeoDetector models were combined to estimate Nr losses in the TLB from 1990 to 2020 and explore driving forces. Different scenarios for Nr losses were compared, showing that Nr loss peaked at 181.66 × 10³ t in 2000. The key factors affecting Nr loss are land use, followed by elevation, soil, and slope factors, and their mean q-values were 0.82, 0.52, 0.51, and 0.48, respectively. The scenario analysis revealed that Nr losses increased under the business-as-usual and economic development scenarios, while ecological conservation, increased nutrient use efficiency, and reduced nutrient application all contribute to a reduction in Nr losses. The findings provide a scientific reference for Nr loss control and future planning in the TLB.

Scenario Simulation of the Relationship between Land-Use Changes and Ecosystem Carbon Storage: A Case Study in Dongting Lake Basin, China

High-frequency land-use changes caused by rapid economic development have become a key factor in the imbalance of carbon sequestration within regions. How to balance economic development and ecological protection is a difficult issue for regional planning. Studying the relationship between future land-use changes and ecosystem carbon storage (CS) is of important significance for the optimization of regional land-use patterns. The research used the gray prediction model and coupled the patch-generating land-use simulation (PLUS) model and the integrated valuation of ecosystem services and trade-offs (InVEST) model. On this basis, the evolution characteristics and spatial coordination between land-use changes and CS in the Dongting Lake Basin (DLB) in different scenarios in 2030 were simulated. The results show that: (1) The spatial distribution of CS remains stable in different scenarios, while land-use types with high carbon density in the periphery of cities are constantly invaded by construction land, which results in the greatest carbon loss in the urban areas. (2) Compared with the natural evolution scenario (NES), only 195.19 km² of land-use types with high carbon density are transformed into construction land in the ecological protection scenario (EPS), generating a carbon sink gain of 182.47 × 10⁴ Mg. Conversely, in the economic development scenario (EDS), a total of over 1400 km² of farmland and ecological land are transformed into construction land, which weakens the carbon sequestration capacity of ecosystems, and more than 147 × 10⁴ Mg of carbon loss occurs in the urban areas. (3) The planned development scenario (PDS) takes ecological protection and economic development both into consideration, which not only generates a carbon sink gain of 121.33 × 10⁴ Mg but also reduces the carbon loss in urban areas by more than 50%. The PDS performs well in both land use and CS growth and can better motivate the effect of land-use changes in increasing the carbon sink, which is also proved by analysis of the coordination between land-use intensity (LUI) and CS. Therefore, the PDS better satisfies the future development demand of DLB and can provide a reference for sustainable land use in the basin.

The role of land use change in affecting ecosystem services and the ecological security pattern of the Hexi Regions, Northwest China

The land use and land cover change (LUCC) associated with climate change and human activities is supposed to exert a significant effect on ecosystem functions in arid inland regions. However, the role of LUCC in shaping the spatio-temporal patterns of ecosystem services and ecological security remain unclear, especially under different future LUCC scenarios. Here, we evaluated dynamic changes of ecosystem services and ecological security pattern (ESP) in the Hexi Regions based on LUCC and other environment variables by integrating morphological spatial pattern analysis (MSPA), entropy weight method and circuit theory. Our result showed that the LUCC was generally stable from 1980 to 2050. Compare to 2020, the land conversion under natural growth (NG), ecological protection (EP) and urban development (UD) scenarios in 2050 has changed by 10.30 %, 10.10 %, and 10.31 %, respectively. The forest, medium-cover grassland and water increased in the EP scenario, and construction land and cropland greatly expanded in the other two scenarios. Ecosystem services grew larger in the EP scenario by 2050 in comparison with the NG and UD scenarios. The ESP in the Hexi Regions has obvious spatial differences during 1980-2050. The larger ecological sources and less resistance corridors were mainly distributed in the central and eastern of the Hexi Regions with high ecosystem services. Conversely, fragmented ecological sources and larger resistance corridors were mostly located in the western regions blocked by sandy land, bare land or mountains. Compared to 2020, the area of ecological sources and pinch points under the EP scenario in 2050 increased by 4.10 × 10³ km² and 0.31 × 10³ km², respectively. The number of ecological corridors reduced while the length and resistance increased apart from the EP scenario. Our results highlighted the importance of ecological protection in shaping the LUCC, which further enhances the integrity of ecosystem and ecological security.

Prediction of Spatiotemporal Changes in Sloping Cropland in the Middle Reaches of the Yangtze River Region under Different Scenarios

With the rapid urban expansion and extensive occupation of cropland, sloping cropland has become an important cropland resource across China. How sloping cropland will change under different socioeconomic scenarios is poorly understood. Therefore, we modeled land-cover change using SSP-RCP multi-scenario simulations and analyzed the evolution and driving factors of sloping cropland change in the middle reaches of the Yangtze River Region (MRYRR). The results indicate the following: In the past twenty years, the cropland and sloping cropland areas in this region declined but the proportion of sloping cropland in total area has been increasing. The average slope of sloping cropland has increased from 7.95° to 8.28°. By 2035, the sloping cropland and total cropland areas will continue to decrease according to the current trend (SSP2-4.5). The average slope will increase maximally to 8.63° under the SSP4-3.4 scenario and minimally to 8.45° under the SSP4-6.0 scenario. Under SSP4-3.4, the extent of slope increase will exceed that in 2005-2010, when regional cropland slope showed the strongest increase in the past. Among 14 social, economic, and ecological factors, average annual precipitation and GDP contributed the most to the change in sloping cropland. This study provides support for decision-making in sustainable land resource allocation to balance urban expansion and cropland conservation.

Projections of land use change and habitat quality assessment by coupling climate change and development patterns

Exploring future land use changes and assessing the habitat quality remains a challenging topic for watershed ecological sustainability. However, most studies ignore the effects of coupled climate change and development patterns. In this study, a framework for assessing habitat quality under the influence of future land use change is constructed based on exploring the driving forces of land use change factors and integrating the system dynamics (SD) model, future land use simulation (FLUS) model and InVest model. The framework enables the projection of land use change and the assessment of habitat quality in the context of future climate change and different development strategies. Applying the framework to the Weihe River Basin, the main driving forces of land-use change in the Weihe River Basin were identified based on geographical detectors, and habitat quality assessment was realized for the Weihe River Basin under the coupled scenarios of three typical shared socioeconomic pathways and future development patterns (SSP126-EP, SSP245-ND, SSP585-EG). The results show that 1) population, precipitation, and temperature are the major driving factors for land use change. 2) The coupling model of SD and FLUS can effectively simulate the future trend of land use change, the relative error is within 2 %, and the overall accuracy is 93.58 %. 3) Significant differences in habitat quality as a result of modifications in land use patterns in different contexts. Affected by ecological protection, the habitat quality in SSP126-EP was significantly better than that in SSP245-ND and SSP585-EG. This research can provide references for future watershed ecological management decisions.

Peaking Global and G20 Countries' CO2 Emissions under the Shared Socio-Economic Pathways

Mitigating climate change requires long-term global efforts. The aim of this study is to simulate the possible paths of CO₂ emissions in G20 countries and the world from 2020 to 2050, by using the STIRPAT model and SSP scenarios with different constraints (SSP baseline, SSP-3.4). The results show that: (1) the world's CO₂ emissions cannot peak in the SSP baseline scenarios, but can peak in the SSP-3.4 scenarios through four paths other than the high fossil energy consumption path; (2) for G20 countries, in the SSP baseline scenarios, 13 countries such as China, the United States, and the United Kingdom can achieve the peak, while six countries such as Argentina, India, and Saudi Arabia cannot. In the SSP-3.4 scenarios, Saudi Arabia cannot achieve the peak, while other countries can achieve the peak, and most of them are likely to achieve significant CO₂ emission reductions by 2050; (3) climate goals have a crowding-out effect on other sustainable development goals, with less impact on developed countries and a greater impact on developing countries; and (4) the optimization of the energy structure and a low energy intensity can greatly advance the peak time of CO₂ emissions.

Multi-Scenario Simulation of Ecosystem Service Values in the Guanzhong Plain Urban Agglomeration, China

Rapid urbanization and human activities enhanced threats to the degradation of various ecosystem services in modern urban agglomerations. This study explored the response of ecosystem service values (ESVs) to land use changes and the trade-offs among various ESVs in urban agglomerations under different future development scenarios. The patch-general land use simulation (PLUS) model and ESV calculation method were used to simulate the ESVs of Guanzhong Plain Urban Agglomeration under the Business As Usual scenario (BAU), Ecological Conservation scenario (EC), and Economic Development scenario (ED) in 2030. Global and local Moran’s I were used to detect the spatial distribution pattern, and correlation analysis was used to measure trade-offs among ecosystem services. The results showed that: (1) The simulated result of land use in Guanzhong Plain Urban Agglomeration showed high accuracy compared to the actual observed result of the same period, with a Kappa coefficient of 0.912. From 2000 to 2030, land use changes were significant, with the rapid decrease in farmland and an increase in construction land. The area of woodland increased significantly under the EC scenario, and the area of construction land increased rapidly under the ED scenario. (2) The decline of total ESV was CNY 218 million from 2000 to 2020, and ESVs remained the downward trend in the BAU and ED scenarios compared to 2020, decreasing by CNY 156 million and CNY 4731 million, respectively. An increasing trend of ESV showed under the EC scenario, with a growth of CNY 849 million. (3) Significant spatial autocorrelation showed in Guanzhong Plain Urban Agglomeration, as the Global Moran’s I were all positive and the p-values were zero. The ESV grids mainly showed “High-High” clusters in the mountainous areas and “Low-Low” clusters in plain areas. Except for food production, a majority of ecosystem services exhibited positive synergistic relationships. In future planning and development, policymakers should focus on the coordinated development of the urbanization process and ecological preservation to build an ecological safety pattern.